Dataset: Deformation twinning and detwinning in extruded Mg-4Al




Files
62
15.58 GB
Samples
0
in dataset
Workflows
0
documented
Views
902
total views
Downloads
210
total downloads
Published
May 16, 2022
4 years ago

File Types

62 files across 4 type(s)

Composition

Structural breakdown of dataset contents

Engagement Over Time

Unique views and downloads per month

Published
Published: 4 years ago
Views
902
Downloads
210
Total Size
15.58 GB
Description

Deformation twinning and detwinning in extruded Mg-4Al were investigated using in-situ SEM-DIC experiments and crystal plasticity finite element (CPFE) simulation. In this study, the in-situ SEM-DIC method was used to provide a unique set of data including twin/detwin characteristics and twin area fraction in addition to strain maps. A statistical analysis of the activation of twin variants and twin area fraction during both twinning and detwinning was conducted. A strong correlation was found between twin growth/shrinkage and the Schmid Factor (SF) for individual twin variants Higher twin SF during loading and unloading led to higher twin growth and shrinkage, respectively. However, after the applied compressive strain was removed, the pattern of the twin area fractions of the residual twin variants versus their nominal SFs did not follow the trend observed at the maximum compressive strain. Using a systematic methodology and an advanced twin/detwin model, the PRISMS-Plasticity CPFE simulation was calibrated using experimentally determined stress versus strain and twin area fraction versus strain information. A comprehensive evaluation of the CPFE model was conducted to determine its ability to capture the statistics of twin variants activation and twin area fraction. CPFE accurately captured the statistical aspects of both twinning and detwinning. It also predicted the first dominant twin variant for 47.5% of the grains and at least one of the two dominant twin variants for 80% of the grains at maximum compressive strain.

Binary 3 Image 17 Text 30 Unknown 12
Citations

No citation data found for this dataset or its associated papers.

Funding
This work was supported by the U.S. Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under Award#DE-SC0008637 as part of the Center for Predictive Integrated Structural Materials Science (PRISMS Center) at University of Michigan. We also acknowledge the financial cost-share support of University of Michigan College of Engineering. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation grant number ACI-1548562, through the allocation TG-MSS160003.